Maximum Consensus Localization Using Lidar Sensors

Abstract

Abstract. Real world localization tasks based on LiDAR usually face a high proportion of outliers arising from erroneous measurements and changing environments. However, applications such as autonomous driving require a high integrity in all of their components, including localization. Standard localization approaches are often based on (recursive) least squares estimation, for example, using Kalman filters. Since least squares minimization shows a strong susceptibility to outliers, it is not robust.In this paper, we focus on high integrity vehicle localization and investigate a maximum consensus localization strategy. For our work, we use 2975 epochs from a Velodyne VLP-16 scanner (representing the vehicle scan data), and map data obtained using a Riegl VMX-250 mobile mapping system. We investigate the effects of varying scene geometry on the maximum consensus result by exhaustively computing the consensus values for the entire search space. We analyze the deviations in position and heading for a circular course in a downtown area by comparing the estimation results to a reference trajectory, and show the robustness of the maximum consensus localization.

Leveraging Dynamic Objects for Relative Localization Correction in a Connected Autonomous Vehicle Network

“…As a summary, the optimum relative errors ∆x, ∆y, ∆θ should be searched in a large 3D space. It is time-consuming to conduct a refined search using a method like maximum consensus (Axmann and Brenner, 2021), although it can provide the global optimum if the maximum search space is previously known. For the example shown in figure 1, the search space is about 12m × 12m × 10°.…”